Skip to main content

Designing Ships Using Constrained Multi-objective Efficient Global Optimization

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11331)

Abstract

A modern ship design process is subject to a wide variety of constraints such as safety constraints, regulations, and physical constraints. Traditionally, ship designs are optimized in an iterative design process. However, this approach is very time consuming and is likely to get stuck in local optima. Not only does this optimization problem have complex constraints, it also consists of multiple objectives like resistance, stability and cost.

This constrained multi-objective optimization problem can be dealt with much more efficiently than through the traditional approach. In this paper, we propose a novel global optimization algorithm that explores the design space with the help of integrated software tools that are capable of simultaneous evaluation of the ship objectives and constraints. The optimization algorithm proposed uses the -Metric-Selection-based Efficient Global Optimization (SMS-EGO) in combination with constraint handling techniques from an algorithm called Self-Adjusting Constrained Optimization by Radial Basis Function Approximation (SACOBRA). Since the evaluation of these ship designs is expensive in terms of computational effort, it is crucial for the algorithm to find feasible near-optimal solutions in as few evaluations as possible.

In this paper, it is shown that the proposed Constrained Efficient Global Optimization (CEGO) algorithm can significantly improve ship designs by automatic optimization using a small evaluation budget.

Keywords

  • Efficient Global Optimization
  • Multi-objective optimization
  • Constrained Optimization
  • Real-world applications

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-13709-0_16
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-13709-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

Notes

  1. 1.

    NAPA Oy, Release 2017.3-3 (2018), NAPA software, http://www.NAPA.fi/.

  2. 2.

    C-Job Naval Architects, Ship Design and Engineering (2018), https://c-job.com/.

References

  1. Bagheri, S., Konen, W., Emmerich, M., Bäck, T.: Self-adjusting parameter control for surrogate-assisted constrained optimization under limited budgets. Appl. Soft Comput. 61, 377–393 (2017)

    CrossRef  Google Scholar 

  2. Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3), 1653–1669 (2007)

    CrossRef  Google Scholar 

  3. Coello, C.A.C., Lamont, G.B., Van Veldhuizen, D.A., et al.: Evolutionary Algorithms for Solving Multi-objective Problems, vol. 5. Springer, New York (2007). https://doi.org/10.1007/978-0-387-36797-2

    CrossRef  MATH  Google Scholar 

  4. De Winter, R.: CEGO: Constrained Multi-Objective Efficient Global Optimization (2018). https://github.com/RoydeZomer/CEGO/

  5. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms, vol. 16. Wiley, Hoboken (2001)

    MATH  Google Scholar 

  6. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., et al. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45356-3_83

    CrossRef  Google Scholar 

  7. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)

    CrossRef  Google Scholar 

  8. Deb, K., Pratap, A., Meyarivan, T.: Constrained test problems for multi-objective evolutionary optimization. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds.) EMO 2001. LNCS, vol. 1993, pp. 284–298. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44719-9_20

    CrossRef  Google Scholar 

  9. Evans, J.H.: Basic design concepts. J. Am. Soc. NavalEngineers 71(4), 671–678 (1959). https://doi.org/10.1111/j.1559-3584.1959.tb01836.x

    CrossRef  Google Scholar 

  10. Gong, W., Cai, Z., Zhu, L.: An efficient multiobjective differential evolution algorithm for engineering design. Struct. Multidiscip. Optim. 38(2), 137–157 (2009)

    CrossRef  Google Scholar 

  11. Jain, H., Deb, K.: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part ii: Handling constraints and extending to an adaptive approach. IEEE Trans. Evol. Comput. 18(4), 602–622 (2014)

    CrossRef  Google Scholar 

  12. Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Glob. Optim. 13(4), 455–492 (1998)

    MathSciNet  CrossRef  Google Scholar 

  13. Knowles, J.: ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans. Evol. Comput. 10(1), 50–66 (2006)

    CrossRef  Google Scholar 

  14. Krige, D.G.: A statistical approach to some basic mine valuation problems on the witwatersrand. J. Chem. Metall. Min. Soc. S. Afr. 52(6), 119–139 (1951)

    Google Scholar 

  15. McKay, M.D., Beckman, R.J., Conover, W.J.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2), 239–245 (1979). http://www.jstor.org/stable/1268522

    MathSciNet  MATH  Google Scholar 

  16. Mirjalili, S., Jangir, P., Saremi, S.: Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl. Intell. 46(1), 79–95 (2017)

    CrossRef  Google Scholar 

  17. Müller, J.: Socemo: surrogate optimization of computationally expensive multiobjective problems. INFORMS J. Comput. 29(4), 581–596 (2017)

    MathSciNet  CrossRef  Google Scholar 

  18. International Maritime Organization: Adoption of the inital IMO strategy on reduction of GHG emissions from ships. Note by the IMO to the 48 session of subsidiary body of scientific and technological advice, Bonn, Germany (2018)

    Google Scholar 

  19. Papanikolaou, A., Harries, S., Wilken, M., Zaraphonitis, G.: Integrated design and multiobjective optimization approach to ship design. In: Proceedings of International Conference on Computer Application in Shipbuilding, vol. 3 (2011)

    Google Scholar 

  20. Parsons, M.G., Scott, R.L.: Formulation of multicriterion design optimization problems for solution with scalar numerical optimization methods. J. Ship Res. 48(1), 61–76 (2004)

    Google Scholar 

  21. Picheny, V.: Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction. Stat. Comput. 25(6), 1265–1280 (2015)

    MathSciNet  CrossRef  Google Scholar 

  22. Ponweiser, W., Wagner, T., Biermann, D., Vincze, M.: Multiobjective optimization on a limited budget of evaluations using model-assisted \(\cal{S}\)-metric selection. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 784–794. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87700-4_78

    CrossRef  Google Scholar 

  23. Powell, M.J.: A direct search optimization method that models the objective and constraint functions by linear interpolation. In: Gomez, S., Hennart, J.P. (eds.) Advances in Optimization and Numerical Analysis. Mathematics and Its Applications, pp. 51–67. Springer, Heidelberg (1994). https://doi.org/10.1007/978-94-015-8330-5_4

    CrossRef  Google Scholar 

  24. Riquelme, N., Von Lücken, C., Baran, B.: Performance metrics in multi-objective optimization. In: 2015 Latin American Computing Conference (CLEI), pp. 1–11. IEEE (2015)

    Google Scholar 

  25. Sederberg, T.W., Parry, S.R.: Free-form deformation of solid geometric models. ACM SIGGRAPH Comput. Graph. 20(4), 151–160 (1986)

    CrossRef  Google Scholar 

  26. Tahara, Y., Stern, F., Himeno, Y.: Computational fluid dynamics-based optimization of a surface combatant. J. Ship Res. 48(4), 273–287 (2004)

    Google Scholar 

  27. Tanabe, R., Oyama, A.: A note on constrained multi-objective optimization benchmark problems. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1127–1134. IEEE (2017)

    Google Scholar 

  28. Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary computation and convergence to a pareto front. In: Late Breaking Papers at the Genetic Programming 1998 Conference, pp. 221–228 (1998)

    Google Scholar 

  29. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. In: EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece, pp. 95–100 (2001)

    Google Scholar 

  30. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Roy de Winter .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

de Winter, R., van Stein, B., Dijkman, M., Bäck, T. (2019). Designing Ships Using Constrained Multi-objective Efficient Global Optimization. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-13709-0_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13708-3

  • Online ISBN: 978-3-030-13709-0

  • eBook Packages: Computer ScienceComputer Science (R0)